I have a small problem with the idea of ‘stories’ and data when it comes to data visualisation. To me, a story is a construct – a neat beginning, middle and end that enables us humans to relay information to each other. The power of stories in human communication is extraordinary. They inspire, they motivate, they change lives. But narratives have a flaw. They don’t need to be right. They don’t need to be accurate or true. The only requirement is to be packaged in a way that makes the audience sit up and listen. This is the reason why TED talks have been so successful, yet so criticised. They are brilliant as a means of conveying information to the audience, but in creating the story behind the presentation, so much may be left out. The audience legitimately might ask ‘that seems almost too perfect. What are they not telling us?’.

Such it is with presenting data. Data is messy. It’s often wrong or inaccurate. It may be tied to a particular question, which is different to the question you are trying to ask. It may show answers that are unintuitive and inconvenient. Data is at war with narrative, or more precisely, it doesn’t care about narrative.

﻿So when presenting your data, be sensitive to the clash between the story you would like to show and what the data is saying (or not saying). As a rule, when presenting data honestly, you should start with everything. Give your audience a chance to see the bigger picture in all its glory and chaos before you dive into the detail. Allow them to ask questions, and work at creating a consensus. Where you see something interesting, gain agreement with them that they can see it too. Be alert to questions from them that might lead to new investigations and new interpretations.

Your job as a data presenter is to show signals in noise, not to eliminate the noise completely. By eliminating the inherent messiness of data for the supposed benefit of the audience, you might just insult their intelligence instead. You also step down a path of deception – careful editing of information – so uncomfortable questions need not be asked.

That’s the problem with stories and data. Balancing the clean and packaged with the messy and inconvenient. To tell data stories properly you should be prepared to take people on a journey whose end is undecided, whose conclusions are tentative at best. Give your audience a chance to find their own meanings and be sensitive for differing interpretations.